Generative-Engine-Marketing/GEM-Bench
First comprehensive benchmark for Generative Engine Marketing (GEM), an emerging field that focuses on monetizing generative AI by seamlessly integrating advertisements into Large Language Model (LLM) responses. Our work addresses the core problem of ad-injected response (AIR) generation and provides a framework for its evaluation.
This project helps marketers and product managers understand how well AI chatbots and search engines can blend ads into their responses without disrupting the user experience. You provide AI-generated text and ad content, and it evaluates the quality, relevance, and user engagement of the ad-injected responses. This tool is designed for professionals who are developing or evaluating generative AI marketing strategies.
Use this if you need to benchmark and improve how ads are integrated into generative AI outputs, ensuring they are effective and non-intrusive.
Not ideal if you are looking for a tool to generate ad content itself, as its primary purpose is evaluation, not creation.
Stars
15
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 18, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/generative-ai/Generative-Engine-Marketing/GEM-Bench"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Featured in
Higher-rated alternatives
openvinotoolkit/model_server
A scalable inference server for models optimized with OpenVINO™
madroidmaq/mlx-omni-server
MLX Omni Server is a local inference server powered by Apple's MLX framework, specifically...
NVIDIA-NeMo/Guardrails
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based...
generative-computing/mellea
Mellea is a library for writing generative programs.
rhesis-ai/rhesis
Open-source platform & SDK for testing LLM and agentic apps. Define expected behavior, generate...